Sai Kosaraju

PINT predicts clinical outcomes from gene expression by modeling pathway-pathway interactions, outperforming benchmarks and revealing biologically meaningful pathway relationships.

This paper introduces FairHICON, a fairness-aware supervised hierarchical contrastive learning method that identifies sex-specific and common genomic drivers, improving predictive performance and reducing sex-based performance gaps in cancer and asthma datasets.

PIMO improves multi-omics integration by modeling pathway-based interactions, enhancing survival analysis and interpretability in cancer datasets.

This paper introduces BIN-PU, a PU learning framework for predicting bacterial protein-compound interactions using only positive samples, showing improved performance over existing methods.

CholBindNet predicts cholesterol binding sites on membrane proteins using interpretable deep learning, outperforming existing models and aligning with molecular dynamics simulations.